The effects of disability status and perceived neighbourhood cohesion and safety on adverse childhood experiences among college students
Bibliographic record
Abstract
Adverse childhood experiences (ACEs) are early traumatic events that can have adverse long-term developmental effects on a person's health and well-being. Individuals with disabilities are at a greater risk of all types of ACEs. However, the impact of having a disability and neighbourhood context on ACEs is under-researched, and even less is known about whether neighbourhood cohesion and safety affect the relationship between disability status and ACEs. The purpose of this study is to examine the direct and indirect pathways between disability status, childhood neighbourhood environment and ACEs. The final study sample of this study was 2,049 college students, consisting of 494 students with disabilities and 1,555 students without disabilities from six universities in the U.S. and Canada between March 2016 and June 2017. Data analysis included Pearson correlations and structural equation modelling procedures using Stata 16 software to test a partial mediation model. Having a disability has both a direct effect and an indirect effect through the neighbourhood environment on ACEs after controlling for socio-demographic characteristics associated with neighbourhood environment or ACEs. The findings suggest that neighbourhood cohesion and safety can be a mediator between disability status and ACEs, and the potential cumulative risk and protective factors that can contribute to ACEs. To elucidate the relationship between disability status and a higher risk for ACEs fully and prevent ACEs that can negatively impact the long-term health outcomes, greater attention to environmental risk and protective factors is urgently needed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".